在放射学可持续性决策中利用大型语言模型
Viktoria Palm1,2,3, Patricia Leutz-Schmidt1,2,3, René Michael Mathy1
1Clinic for Diagnostic and Interventional Radiology (DIR), Heidelberg University Hospital, Heidelberg, Germany.
Frontiers in medicine
|October 16, 2025
概括
大型语言模型 (LLM) 显示出在放射学中提供可持续性建议的巨大潜力. 克劳德 3.5 索内特表现最好,证明了LLMs.
科学领域:
- 环境科学 环境科学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 放射学对环境产生重大影响,但实际的可持续性指导很少.
- 大型语言模型 (LLM) 为传播可持续性最佳实践提供了一个潜在的解决方案.
研究的目的:
- 评估最先进的LLM在为放射学提供可持续性建议方面的表现.
- 为了比较聊天GPT-4.0的有效性,克劳德3.5索内特,双子座高级和Meta Llama 3.1.
主要方法:
- 通过30个关于放射学可持续性的标准化问题,评估了四个领先的LLM.
- 经验丰富的读者对质量,可理解性和可实施性进行了评分.
- 平均质量分数 (MQS) 计算以确定整体绩效.
主要成果:
- 所有评估的LLM在可理解性方面表现良好至非常好.
- 克劳德 3.5 索内特获得了最高的整体平均质量得分 (3.95 ± 0.22).
- 接下来是Meta Llama 3.1,ChatGPT-4.0和Gemini Advanced,没有观察到没有足够的评级.
结论:
- 士课程显示出在放射学中推进可持续实践的巨大潜力.
- 模型选择至关重要,因为不同LLM的表现各不相同.
- 克劳德 3.5 索内特,Meta Llama 3.1 和ChatGPT-4.0是放射学可持续性倡议的有希望的工具.
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